Characterization of the Partial Autocorrelation Function

The paper focused on Autoregressive modeling and forecasts of Degema Local Government Council Monthly Allocation (DLGCMA) in River State, Nigeria. The Buys-Ballot table and Bartlett’s Transformation method were adopted to identify the trend pattern and to determine the best transformation for the series. The logarithmic transformation was adjudged to be the best and was applied to stabilize the variance. Identification of the trend and stationary for the data set was done and the DLGCMA series showed a linear trend that was non-stationary. The stationarity of the DLGCMA series was obtained after the first difference. The ARIMA models were fitted to the series base on the behaviour of autocorrelation function (ACF) and partial autocorrelation function (PACF). Finally, the model selection criteria called Akaike information criterion was used to determine the best model among the predicted models. The AR(3,1,0) model ( Xt = 0.56Xt-1 + 0.17Xt-2 + 0.64Xt-3 - 0.37Xt-4 + et) was considered to be the best model because it has the least value of the Akaike information criterion (AIC). Hence, the forecasts for the next allocation of twenty-four (24) months ahead were determined.

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Characterization of quantum chaos by the autocorrelation function of spectral determinants

Journal of Physics A General Physics ◽

10.1088/0305-4470/30/10/035 ◽

1997 ◽

Vol 30 (10) ◽

pp. 3643-3662 ◽

Cited By ~ 26

Author(s):

S Kettemann ◽

D Klakow ◽

U Smilansky

Keyword(s):

Autocorrelation Function ◽

Quantum Chaos

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Research Data Analysis Using EViews

Advances in Library and Information Science - Research Data Access and Management in Modern Libraries ◽

10.4018/978-1-5225-8437-7.ch014 ◽

2019 ◽

pp. 292-324

Author(s):

Erginbay Uğurlu

Keyword(s):

Autocorrelation Function ◽

Academic Writing ◽

Unit Root Test ◽

Garch Models ◽

Arch Model ◽

Arch Models ◽

Arch Effect ◽

Lm Test ◽

Partial Autocorrelation Function ◽

Stock Indexes

The aim of this chapter is to provide a detailed empirical example of autoregressive conditional heteroskedasticity (ARCH) model and selected generalized ARCH models. Before the ARCH/GARCH models are estimated, several calculations and tests should be done. The mean model is determined using the autocorrelation function and partial autocorrelation function and also the unit root test. The existence of ARCH effect is tested using ARCH-LM test. After these steps are done, then ARCH/GARCH models can be estimated. All these theoretical aspects are applied to Sofia Stock Indexes (SOFIX) using EViews 9 software package. The windows and output of EViews are presented. To show the output's academic writing format researchers' outputs are presented in a table.

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Predicting Daily Confirmed COVID-19 Cases in India

Advances in Medical Technologies and Clinical Practice - Machine Learning and Data Analytics for Predicting, Managing, and Monitoring Disease ◽

10.4018/978-1-7998-7188-0.ch003 ◽

2021 ◽

pp. 34-51

Author(s):

Sudip Singh

Keyword(s):

Time Series ◽

Root Mean Square Error ◽

Autocorrelation Function ◽

Moving Average ◽

Arima Model ◽

Family Welfare ◽

Mean Square ◽

Univariate Time Series ◽

Partial Autocorrelation Function ◽

Auto Regressive

India, with a population of over 1.38 billion, is facing high number of daily COVID-19 confirmed cases. In this chapter, the authors have applied ARIMA model (auto-regressive integrated moving average) to predict daily confirmed COVID-19 cases in India. Detailed univariate time series analysis was conducted on daily confirmed data from 19.03.2020 to 28.07.2020, and the predictions from the model were satisfactory with root mean square error (RSME) of 7,103. Data for this study was obtained from various reliable sources, including the Ministry of Health and Family Welfare (MoHFW) and http://covid19india.org/. The model identified was ARIMA(1,1,1) based on time series decomposition, autocorrelation function (ACF), and partial autocorrelation function (PACF).

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Forecasting Rice Productivity and Production of Odisha, India, Using Autoregressive Integrated Moving Average Models

Advances in Agriculture ◽

10.1155/2014/621313 ◽

2014 ◽

Vol 2014 ◽

pp. 1-9

Author(s):

Rahul Tripathi ◽

A. K. Nayak ◽

R. Raja ◽

Mohammad Shahid ◽

Anjani Kumar ◽

...

Keyword(s):

Autocorrelation Function ◽

Moving Average ◽

Selection Criterion ◽

Information Criteria ◽

Arima Models ◽

Autoregressive Integrated Moving Average ◽

Rice Productivity ◽

Rice Area ◽

Moving Average Models ◽

Partial Autocorrelation Function

Forecasting of rice area, production, and productivity of Odisha was made from the historical data of 1950-51 to 2008-09 by using univariate autoregressive integrated moving average (ARIMA) models and was compared with the forecasted all Indian data. The autoregressive (p) and moving average (q) parameters were identified based on the significant spikes in the plots of partial autocorrelation function (PACF) and autocorrelation function (ACF) of the different time series. ARIMA (2, 1, 0) model was found suitable for all Indian rice productivity and production, whereas ARIMA (1, 1, 1) was best fitted for forecasting of rice productivity and production in Odisha. Prediction was made for the immediate next three years, that is, 2007-08, 2008-09, and 2009-10, using the best fitted ARIMA models based on minimum value of the selection criterion, that is, Akaike information criteria (AIC) and Schwarz-Bayesian information criteria (SBC). The performances of models were validated by comparing with percentage deviation from the actual values and mean absolute percent error (MAPE), which was found to be 0.61 and 2.99% for the area under rice in Odisha and India, respectively. Similarly for prediction of rice production and productivity in Odisha and India, the MAPE was found to be less than 6%.

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